{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实战FashionMNIST分类，在训练过程中加入更多的控制\n",
    "\n",
    "1. 训练中保存/保存最好的模型\n",
    "2. 早停 \n",
    "3. 模型加载\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T04:12:49.707940Z",
     "start_time": "2025-01-26T04:12:45.990638Z"
    }
   },
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "    \n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)  #设备是cuda:0，即GPU，如果没有GPU则是cpu\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=12, micro=3, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.1\n",
      "torch 2.5.1+cpu\n",
      "cpu\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": "# 数据准备"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T04:36:25.658654Z",
     "start_time": "2025-01-26T04:36:23.993622Z"
    }
   },
   "source": [
    "from torchvision import datasets\n",
    "from torchvision.transforms import ToTensor\n",
    "\n",
    "# fashion_mnist图像分类数据集\n",
    "train_ds = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=ToTensor()\n",
    ")\n",
    "\n",
    "test_ds = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=ToTensor()\n",
    ")"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T04:36:25.925976Z",
     "start_time": "2025-01-26T04:36:25.922547Z"
    }
   },
   "source": [
    "# 从数据集到dataloader\n",
    "train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64, shuffle=True)\n",
    "val_loader = torch.utils.data.DataLoader(test_ds, batch_size=64, shuffle=False)"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "source": [
    "# 查看数据\n",
    "for datas, labels in train_loader:\n",
    "    print(datas.shape)\n",
    "    print(labels.shape)\n",
    "    break\n",
    "#查看val_loader\n",
    "for datas, labels in val_loader:\n",
    "    print(datas.shape)\n",
    "    print(labels.shape)\n",
    "    break"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-26T04:37:32.572430Z",
     "start_time": "2025-01-26T04:37:32.539686Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 1, 28, 28])\n",
      "torch.Size([64])\n",
      "torch.Size([64, 1, 28, 28])\n",
      "torch.Size([64])\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T04:37:43.816685Z",
     "start_time": "2025-01-26T04:37:43.808928Z"
    }
   },
   "source": [
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.linear_relu_stack = nn.Sequential(\n",
    "            nn.Linear(28 * 28, 300),  # in_features=784, out_features=300\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(300, 100),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(100, 10),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x.shape [batch size, 1, 28, 28]\n",
    "        x = self.flatten(x)  \n",
    "        # 展平后 x.shape [batch size, 28 * 28]\n",
    "        logits = self.linear_relu_stack(x)\n",
    "        # logits.shape [batch size, 10]\n",
    "        return logits\n",
    "    \n",
    "model = NeuralNetwork()"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练\n",
    "\n",
    "pytorch的训练需要自行实现，包括\n",
    "1. 定义损失函数\n",
    "2. 定义优化器\n",
    "3. 定义训练步\n",
    "4. 训练"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T04:40:57.976602Z",
     "start_time": "2025-01-26T04:40:57.902447Z"
    }
   },
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "@torch.no_grad()  # 作用是禁止反向传播，节省内存\n",
    "def evaluating(model, dataloader, loss_fct):\n",
    "    loss_list = []\n",
    "    pred_list = []\n",
    "    label_list = []\n",
    "    for datas, labels in dataloader:\n",
    "        datas = datas.to(device)\n",
    "        labels = labels.to(device)\n",
    "        # 前向计算\n",
    "        logits = model(datas)\n",
    "        loss = loss_fct(logits, labels)         # 验证集损失\n",
    "        loss_list.append(loss.item()) # tensor.item() 获取tensor的数值，loss是只有一个元素的tensor\n",
    "        \n",
    "        preds = logits.argmax(axis=-1)    # 验证集预测, axis=-1 表示最后一个维度,因为logits.shape [batch size, 10]，所以axis=-1表示对最后一个维度求argmax，即对每个样本的10个类别的概率求argmax，得到最大概率的类别, preds.shape [batch size]\n",
    "        pred_list.extend(preds.cpu().numpy().tolist()) # tensor转numpy，再转list\n",
    "        label_list.extend(labels.cpu().numpy().tolist())\n",
    "        \n",
    "    acc = accuracy_score(label_list, pred_list) # 验证集准确率\n",
    "    return np.mean(loss_list), acc # 返回验证集平均损失和准确率\n"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## ModelCheckpoint（保存模型）\n",
    "1. 为了上线\n",
    "2. 避免模型中途训练终止，再次训练可以从上次保存点进行"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Save Best 保存最佳模型权重\n"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T04:45:42.456969Z",
     "start_time": "2025-01-26T04:45:42.451952Z"
    }
   },
   "source": [
    "class SaveCheckpointsCallback:  # 保存最好的模型权重\n",
    "    def __init__(self, save_dir, save_step=500, save_best_only=True):\n",
    "        self.save_dir = save_dir # 保存路径\n",
    "        self.save_step = save_step # 保存步数\n",
    "        self.save_best_only = save_best_only # 是否只保存最好的模型\n",
    "        self.best_metrics = -1 # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        \n",
    "        # 如果不存在保存路径，则创建\n",
    "        if not os.path.exists(self.save_dir): \n",
    "            os.mkdir(self.save_dir)\n",
    "        \n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        \"\"\"\n",
    "        :param step: 当前点的步数\n",
    "        :param state_dict: 模型权重\n",
    "        :param metric: 算子\n",
    "        :return: \n",
    "        \"\"\"\n",
    "        if step % self.save_step > 0:  # 每隔save_step步保存一次\n",
    "            return\n",
    "        \n",
    "        if self.save_best_only:\n",
    "            assert metric is not None # 必须传入metric\n",
    "            if metric >= self.best_metrics:\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"best.ckpt\")) # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                self.best_metrics = metric\n",
    "        else:\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\")) "
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Early Stop（早停）：避免过拟合\n",
    "1. min_delta：最小的提升幅度,绝对变化小于这个参数，就不视为改进\n",
    "2. patience：多少轮没有提升就停止训练"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T04:55:10.967703Z",
     "start_time": "2025-01-26T04:55:10.963090Z"
    }
   },
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience # 多少个轮没有提升就停止训练\n",
    "        self.min_delta = min_delta # 最小的提升幅度\n",
    "        self.best_metric = -1\n",
    "        self.counter = 0  # 计数器，记录多少step没提升\n",
    "        \n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:# 用准确率\n",
    "            self.best_metric = metric\n",
    "            self.counter = 0\n",
    "        else: \n",
    "            self.counter += 1   # 计数器加1，下面的patience判断用到\n",
    "            \n",
    "    @property  # 使用@property装饰器，使得方法变成属性，使得对象.early_stop可以调用，无需加()\n",
    "    def early_stop(self):\n",
    "        return self.counter >= self.patience\n"
   ],
   "outputs": [],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T04:55:11.446500Z",
     "start_time": "2025-01-26T04:55:11.439796Z"
    }
   },
   "source": [
    "# 训练\n",
    "def training(\n",
    "    model, \n",
    "    train_loader, \n",
    "    val_loader, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    save_ckpt_callback=None,\n",
    "    early_stop_callback=None,\n",
    "    eval_step=500,\n",
    "    ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "    \n",
    "    global_step = 0\n",
    "    model.train()\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # training\n",
    "            for datas, labels in train_loader:\n",
    "                datas = datas.to(device) # 数据放到device上\n",
    "                labels = labels.to(device) # 标签放到device上\n",
    "                # 梯度清空\n",
    "                optimizer.zero_grad()\n",
    "                # 模型前向计算\n",
    "                logits = model(datas)\n",
    "                # 计算损失\n",
    "                loss = loss_fct(logits, labels)\n",
    "                # 梯度回传，计算梯度，更新参数，这里是更新模型参数\n",
    "                loss.backward()\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                preds = logits.argmax(axis=-1)\n",
    "                acc = accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())    \n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss, \"acc\": acc, \"step\": global_step\n",
    "                })\n",
    "                \n",
    "                # evaluating\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 切换到验证集模式\n",
    "                    val_loss, val_acc = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss, \"acc\": val_acc, \"step\": global_step\n",
    "                    })\n",
    "                    model.train() # 切换回训练集模式\n",
    "                    \n",
    "                    # 1. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), metric=val_acc) # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric(验证集准确率)判断是否保存最好的模型\n",
    "\n",
    "                    # 2. 早停 Early Stop\n",
    "                    if early_stop_callback is not None:\n",
    "                        early_stop_callback(val_acc) # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:# 验证集准确率不再提升，则停止训练\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict\n",
    "                    \n",
    "                # update step\n",
    "                global_step += 1\n",
    "                pbar.update(1)\n",
    "                pbar.set_postfix({\"epoch\": epoch_id})\n",
    "        \n",
    "    return record_dict"
   ],
   "outputs": [],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "source": [
    "epoch = 100\n",
    "model = NeuralNetwork()\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = nn.CrossEntropyLoss()\n",
    "# 2. 定义优化器 采用SGD\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
    "# 3. save best\n",
    "save_ckpt_callback = SaveCheckpointsCallback(\"checkpoints\", save_best_only=True)\n",
    "# 4. early stop\n",
    "early_stop_callback = EarlyStopCallback(patience=20)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-26T05:01:22.195487Z",
     "start_time": "2025-01-26T05:01:22.189816Z"
    }
   },
   "outputs": [],
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "source": "model.state_dict().keys()  # 模型参数名字",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-26T05:01:23.605305Z",
     "start_time": "2025-01-26T05:01:23.600180Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_keys(['linear_relu_stack.0.weight', 'linear_relu_stack.0.bias', 'linear_relu_stack.2.weight', 'linear_relu_stack.2.bias', 'linear_relu_stack.4.weight', 'linear_relu_stack.4.bias'])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 21
  },
  {
   "cell_type": "code",
   "source": [
    "model = model.to(device)  # 放到device上\n",
    "record = training(\n",
    "    model,\n",
    "    train_loader,\n",
    "    val_loader,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=1000\n",
    "    )"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-26T05:08:03.904910Z",
     "start_time": "2025-01-26T05:01:24.482154Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/93800 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "06a0d07d3417447baa5fdf245229af7b"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 63 / global_step 60000\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T04:59:17.872802Z",
     "start_time": "2025-01-26T04:59:17.834853Z"
    }
   },
   "cell_type": "code",
   "source": "record",
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  {'loss': 2.2835357189178467, 'acc': 0.09375, 'step': 16},\n",
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       "  {'loss': 2.2947757244110107, 'acc': 0.078125, 'step': 22},\n",
       "  {'loss': 2.2968990802764893, 'acc': 0.109375, 'step': 23},\n",
       "  {'loss': 2.286705255508423, 'acc': 0.21875, 'step': 24},\n",
       "  {'loss': 2.27699875831604, 'acc': 0.1875, 'step': 25},\n",
       "  {'loss': 2.2867705821990967, 'acc': 0.21875, 'step': 26},\n",
       "  {'loss': 2.2936995029449463, 'acc': 0.21875, 'step': 27},\n",
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       "  {'loss': 0.5023724863863295, 'acc': 0.8218, 'step': 5000},\n",
       "  {'loss': 0.4814131102364534, 'acc': 0.8298, 'step': 6000},\n",
       "  {'loss': 0.48956088968522987, 'acc': 0.8257, 'step': 7000},\n",
       "  {'loss': 0.45728212481091735, 'acc': 0.838, 'step': 8000},\n",
       "  {'loss': 0.4506891449545599, 'acc': 0.8401, 'step': 9000},\n",
       "  {'loss': 0.4487941392288087, 'acc': 0.8419, 'step': 10000},\n",
       "  {'loss': 0.4337083380313436, 'acc': 0.8464, 'step': 11000},\n",
       "  {'loss': 0.4284774912580563, 'acc': 0.8492, 'step': 12000},\n",
       "  {'loss': 0.42447022343896756, 'acc': 0.8492, 'step': 13000},\n",
       "  {'loss': 0.414137865137902, 'acc': 0.8558, 'step': 14000},\n",
       "  {'loss': 0.44119627716814636, 'acc': 0.8448, 'step': 15000},\n",
       "  {'loss': 0.40329475719837626, 'acc': 0.8567, 'step': 16000},\n",
       "  {'loss': 0.407194145545838, 'acc': 0.8568, 'step': 17000},\n",
       "  {'loss': 0.4016768437851766, 'acc': 0.8587, 'step': 18000},\n",
       "  {'loss': 0.4003517370504938, 'acc': 0.8594, 'step': 19000},\n",
       "  {'loss': 0.3901401040660348, 'acc': 0.8639, 'step': 20000},\n",
       "  {'loss': 0.3930203163889563, 'acc': 0.8619, 'step': 21000},\n",
       "  {'loss': 0.3851956679562854, 'acc': 0.8641, 'step': 22000},\n",
       "  {'loss': 0.3823566749976699, 'acc': 0.8639, 'step': 23000},\n",
       "  {'loss': 0.3762928596727408, 'acc': 0.8654, 'step': 24000}]}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T05:35:48.376331Z",
     "start_time": "2025-01-26T05:35:48.189135Z"
    }
   },
   "source": [
    "# 画线要注意的是损失是不一定在0到1之间的\n",
    "def \\\n",
    "        plot_learning_curves(record_dict, sample_step=500):\n",
    "    # build DataFrame\n",
    "    # 字典取出来是列表，列表里是一个一个字典，set_index(\"step\")把step作为索引，iloc[::sample_step]每ample_step步取一行\n",
    "    train_df = pd.DataFrame(record_dict[\"train\"]).set_index(\"step\").iloc[::sample_step]\n",
    "    val_df = pd.DataFrame(record_dict[\"val\"]).set_index(\"step\")\n",
    "    \n",
    "    print(train_df.head())\n",
    "    print(val_df.head())\n",
    "\n",
    "    # plot\n",
    "    fig_num = len(train_df.columns)  # 因为有loss和acc两个指标，所以画2个子图\n",
    "    fig, axs = plt.subplots(1, fig_num, figsize=(10 * fig_num, 10)) #fig_num个子图，figsize是子图大小\n",
    "    for idx, item in enumerate(train_df.columns):    \n",
    "        # index是步数，item是指标名\n",
    "        axs[idx].plot(train_df.index, train_df[item], label=f\"train_{item}\")\n",
    "        axs[idx].plot(val_df.index, val_df[item], label=f\"val_{item}\")\n",
    "        axs[idx].grid()\n",
    "        axs[idx].legend()\n",
    "        x_data=range(0, train_df.index[-1], 5000)  # 每隔5000步标出一个点\n",
    "        axs[idx].set_xticks(x_data)\n",
    "        # map 函数用于将一个函数应用到一个可迭代对象的每个元素上，并返回一个迭代器。比如其内容为[\"1k\", \"2k\", \"5k\", \"7k\", \"10k\"]，则map(lambda x: f\"{int(x/1000)}k\", x_data)的结果为[\"1k\", \"2k\", \"5k\", \"7k\", \"10k\"]\n",
    "        axs[idx].set_xticklabels(map(lambda x: f\"{int(x/1000)}k\", x_data)) # map生成labal\n",
    "        axs[idx].set_xlabel(\"step\")\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "plot_learning_curves(record, sample_step=500)  #横坐标是 steps"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          loss       acc\n",
      "step                    \n",
      "0     2.315466  0.031250\n",
      "500   1.123114  0.687500\n",
      "1000  0.830903  0.828125\n",
      "1500  0.596442  0.796875\n",
      "2000  0.722101  0.765625\n",
      "          loss     acc\n",
      "step                  \n",
      "0     2.311623  0.0265\n",
      "1000  0.814665  0.6906\n",
      "2000  0.636670  0.7792\n",
      "3000  0.580871  0.7976\n",
      "4000  0.524867  0.8152\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 23
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 评估"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "model = NeuralNetwork() #上线时加载模型\n",
    "model = model.to(device)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-26T05:35:59.605141Z",
     "start_time": "2025-01-26T05:35:59.600606Z"
    }
   },
   "outputs": [],
   "execution_count": 25
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T05:36:01.376791Z",
     "start_time": "2025-01-26T05:36:00.532807Z"
    }
   },
   "source": [
    "# 模型保存有2种，一种是保存模型结构和模型参数，一种是只保存模型参数，这里只保存参数，加上weights_only=True\n",
    "# load checkpoints\n",
    "model.load_state_dict(torch.load(\"checkpoints/best.ckpt\", weights_only=True, map_location=\"cpu\"))\n",
    "model.eval()\n",
    "loss, acc = evaluating(model, val_loader, loss_fct)\n",
    "print(f\"loss:     {loss:.4f}\\naccuracy: {acc:.4f}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:     0.3261\n",
      "accuracy: 0.8847\n"
     ]
    }
   ],
   "execution_count": 26
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
